Stock market forecasting using Continuous Wavelet Transform and Long Short-Term Memory neural networks

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The analysis and exploitation of complex and large-volume data requires new approaches, and modeling it in time series is a very successful technique. A characteristic time series is the one that defines the dynamic financial market and its asset prices. This research presents a novel forecasting methodology, which uses the Continuous Wavelet Transform for the definition of representative elements that define a time series, and a recurrent neural network architecture for the forecast of prices of financial stocks related by the item of income in the short and medium time term. The proposed model, inspired by the Continuous Wavelet Transform and Neural Networks of the "Long short-term memory" type, uses the most representative coefficients of the Wavelet transform based on the time series in the time domain, for the prediction of future prices of stocks in short prospective periods. The results show a very successful projection using this methodology. Future research will analyze the interrelationship presented by the price time series of the same stock market section, in the domain of Wavelets, and how it affects the stock market forecast.


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JOSE ALFREDO ACUNA GARCIA, Computer Science Faculty

Computer Science Facult, Autonomous University of Queretaro, Academic personal.


Computer Science Facult, Autonomous University of Queretaro, Academic personal.


Computer Science Facult, Autonomous University of Queretaro, Academic personal.


Parmar K, Bhardwaj R. Trend Time Series and Wavelet Analysis of River Water Dynamics. Non-Linear Dynamics Lab, Department of Mathematics University School of Basic and Applied Sciences Guru Gobind Singh Indraprastha. 2014; https:/

Zhang S, Cong X. The Application of Wavelet Analysis in Financial Multiple Change Points Time Series. 5th International Conference on Industrial Economics System and Industrial Security Engineering. 2018; https:/

Chui C. Wavelet analysis and its applications, USA. Academic Press. 1992; | April 2003.

Acuna-Garcia J, Canchola-Magdaleno S. Análisis de singularidades en líneas de producción utilizando transformada Wavelet en un sistema embebido. Revista Electrónica de Divulgación de la Investigación Vol. 18. 2019.

Andrews W, Ploberger W. Optimal tests when a nuisance parameter is present only under the alternative. Econometrica, vol. 62, pp. 1383-1414. 1994.

Zhang S, Bao G. Tian B, Li Y. Semiparametric test for multiple change-points based on empirical likelihood. Communication in Statistics-Theory and Methods, vol.46, pp. 3574– 3585. 2017; https:/

Zhang S, Tian B. Inference for random coefficient INAR(k) with the occasional level shift random noise based on empirical likelihood. Communication in Statistics-Theory and Methods, vol.46, pp. 6994– 7006. 2017.

Zhang S, Tian B. Semiparametric method for identifying multiple change-points in financial market. Journal Communications in Statistics - Simulation and Computation, vol.4, pp. 2004–2519. 2017; https:/

Lendasse A, Oja E, Simula O, Verleysen M. Time series prediction competition: The CATS benchmark. Neurocomputing, vol. 70, no. 13-15, pp. 2325–2329. 2007; https:/

Dixon M. Industrial Forecasting with Exponentially Smoothed Recurrent Neural Networks. 2020; https:/

Dixon M, London J. Financial Forecasting with alpha-RNNs: A Time Series Modeling Approach. Frontiers in Applied Mathematics and Statistics 6. 2020; https:/

Dixon M, Klabjan D, Bang J. Classification-Based Financial Markets Prediction Using Deep Neural Networks. SSRN Electronic Journal. 2017; https:/

Cai X, Zhang N, Venayagamoorthy G. Time series prediction with recurrent neural networks traindes by hibrid pso-ea algorithm. Neurocomputing, Vol 70, no. 13-15, pp. 2342-2353. 2007; https:/

Beliaev I, Kozma R. Time series prediction using chaotic neural networks on the CATS Benchmark. Neurocomputing, Vol. 70, no.13-15, pp.2426-2439. 2007; https:/

Gómez P, Ramirez M. Expriments with a hibrid-complex neural networks for long term prediction of electrocardiogram. Proceedings of the IEEE 2006 international world congress of computational intelligence. Vancouver Canada. 2006.

Yu Z, Abma R, Etgen J, Sullivan C. Attenuation of noise and simultaneous source interference using wavelet denoising. Society of Exploration Geophysicists. 2017; https:/

Cedeño A, Trujillo R. Esquema basado en Wavelet para la reducción de ruido online en señales industriales. Revista Cubana de Ciencias Informáticas, Vol. 8, No. 3. 2014; https:/

Yaseen A, Pavlov A, Hramov A. Speech signal denoising with wavelet-transforms and the mean opinion score characterizing the filtering quality. Proceedings Volume 9707, Dynamics and Fluctuations in Biomedical Photonics XIII. 2016; https:/

Santamaría F, Cortés C, Román F. Uso de la Transformada de Ondeletas (Wavelet Transform) en la Reducción de Ruidos en las Señales de Campo Eléctrico producidas por Rayos. Información Tecnológica Vol. 23 Nº 1. 2012; https:/

Amosov O, Amosova S, Muller N. Identification of Potential Risks to System Security Using Wavelet Analysis, the Time-and-Frequency Distribution Indicator of the Time Series and the Correlation Analysis of Wavelet-Spectra. International Multi-Conference on Industrial Engineering and Modern Technologies. 2018; https:/

Lone M. Performance Analysis of DWT Families. Conference: 3rd International Conference on Intelligent Sustainable Systems (ICISS) At: Thoothukudi, India, India. 2021; https:/

Larkin K. Structural Similarity Index SSIMplified. Occasional Texts in the Pursuit of Clarity and Simplicity in Research. Series 1, Number 1. 2015.

Gandhi S, Kulkarni C. MSE Vs SSIM. International Journal of Scientific & Engineering Research, Volume 4, Issue 7, July. 2013.